Instructions to use VickFan/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VickFan/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="VickFan/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("VickFan/dummy-model") model = AutoModelForMaskedLM.from_pretrained("VickFan/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| base_model: camembert-base | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: dummy-model | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # dummy-model | |
| This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - optimizer: None | |
| - training_precision: float32 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.40.1 | |
| - TensorFlow 2.15.0 | |
| - Tokenizers 0.19.1 | |